Overview
Prepare for more uncertainty in 2023 with our two-part event series, showing you how to turn data into practical insights. Join us at our Singapore Office to discover the cost-effective, high-quality content needed to transform your enterprise workflow.
The first session has concluded on March 1. Register for the second session on March 21:
Sessions
[Session 2] Actionable Insights: Data Science in Practice
Tuesday, March 21, 2023
At the second event, we put content into action. Hear from a range of industry experts – practitioners, academics, and technologists – each bringing a unique perspective on how data science is realized within their domains. You’ll also discover how to leverage the technology needed to turn vast amounts of data into efficient capital allocation.
- 4:30pm: Welcome Address
– Steven Yankelson, Head of ASEAN, Bloomberg - 4:35pm: Panel Discussion – Theory Meets Practice: Data Science in Finance
– Ritchie Ng, Head of Data & AI, Eastspring Investments
– Dr Li Xuchun, Head of AI Development Office (FinTech & Innovation Group), Monetary Authority of Singapore
– Prof Chao Zhou, Director, Centre for Quantitative Finance, National University of Singapore
– Gary Kazantsev, Global Head of Quant Technology Strategy, Bloomberg
– Ryuji Kanda, Head of Quant Specialists, ASEAN, Bloomberg (moderator) - 5:05pm: Generate Actionable Insights from Textual and Intraday Analytics
– Gary Kazantsev, Global Head of Quant Technology Strategy, Bloomberg
– Steven Chen, Product Manager, CTO Office, Bloomberg - 5:25pm: Scale Up Your Quantitative Investment Strategies on the Cloud
– Ian Hummel, Technical Product Manager, Bloomberg - 5:40pm: Machine Learning and ESG, Generating a Meaningful Output
– Yusuke Tomishima, Data Scientist, Tokio Marine & Nichido Fire Insurance
- 6:00pm: Q&A; Networking to follow
- 4:30pm: Welcome Address
[Session 1] The Road Ahead: Content & Workflow Considerations
Wednesday, March 1, 2023 [Ended]
The first event provides an overview of Bloomberg’s latest releases across ESG, funds, and research for company fundamentals, tick history, and point-in-time analysis. You will learn how to generate value with our unified data model, before hearing from executives at AWS and Snowflake, speaking on cloud trends within financial services and data migration.
Speakers
Yusuke Tomishima
Yusuke Tomishima is the Head of Data Scientist in charge of DX for asset management at Tokio Marine Nichido Fire. He will also serve as a visiting professor of Economics at Tama Graduate School beginning in September 2023. He is specialized in application of AI/ML to investment strategies. Prior to joining Tokio Marine in 2021, he had worked for Prudential Group as a quant in charge of ALM, and for Mizuho Bank as a credit derivatives trader, Japanese stock trader, and Quant fund manager.
Yusuke is the author of many commercially published books, including “Introduction to Finance Theory for Those Who Want to Understand Investment and Finance” (CCC Media House), “Introduction to Index Investing for Automatic Asset Growth” (Nihon Jitsugyo Shuppan), “How to Self-Study Mathematics” (Kodansha Gendai Shinsho), and “Beautiful Mathematics Hidden in Daily Life” (Asahi Shimbun Publications).
Ritchie Ng
Ritchie Ng leads engineering and governance across data, machine learning, and cloud in Eastspring Investments Group. The Data & AI department builds, scales, and maintains Eastspring’s proprietary cloud-native Data & AI platform, Hera. The platform powers every functional division including investments, distribution, ESG, finance, risk, compliance, and COO, enabling Eastspring to be a data-driven organisation.
Ritchie is also a visiting research scholar in NExT++ Lab, a joint AI research lab based in National University of Singapore (NUS) between Tsinghua University and NUS. He works on open-source and proprietary applied deep learning research with multimodal and multilingual data in Asian systematic trading strategies.
Prior to Eastspring Investments, Ritchie programmed end-to-end systems covering low-latency and distributed data engineering, systematic trading, and alpha signal research in systematic global macro hedge funds. Ritchie holds a Master’s degree from Imperial College London and a Bachelor’s degree from NUS.
Dr Li Xuchun
Dr Li is heading the AI Development Office from the FinTech & Innovation Group in MAS. The mandate of the AI Development Office focuses on promoting the usage of Artificial Intelligence in the Singapore financial sector. It will develop and implement the AI strategy for Singapore financial industry, facilitate industry-wide AI projects within and beyond the financial sector as well as embark on initiatives to develop a sustainable ecosystem that supports the experimentation and deployment of AI-based technologies in the financial sector.
Dr Li’s major expertise covers areas such as machine learning, big data analysis, cloud computing, text mining etc. He also has strong commercial software development experience. Before joining MAS, he was a scientist at VISA and UBS. He holds a PhD in machine learning.
Prof Chao Zhou
Gary Kazantsev
Gary is the Head of Quant Technology Strategy in the Office of the CTO at Bloomberg. Prior to taking on this role, he created and headed the company’s Machine Learning Engineering group, leading projects at the intersection of computational linguistics, machine learning and finance, such as sentiment analysis of financial news, market impact indicators, statistical text classification, social media analytics, question answering, and predictive modeling of financial markets.
Prior to joining Bloomberg in 2007, Gary had earned degrees with distinction in physics, mathematics, and computer science from Boston University.
He is engaged in advisory roles with FinTech and Machine Learning startups and has worked at a variety of technology and academic organizations over the last 20 years. In addition to speaking regularly at industry and academic events around the globe, he is a member of the KDD Data Science + Journalism workshop program committee and the advisory board for the AI & Data Science in Trading conference series. He is also an adjunct professor at Columbia University, and a co-organizer of the annual Machine Learning in Finance conference at Columbia University.
Steven Chen
Steven Chen, CFA is Head of Quant Analytics and Data Solutions in Bloomberg’s CTO Office where he sets the product vision and strategy for BQuant’s quant analytics and data products. Steven and his team are responsible for developing and bringing innovative products to market including most recently, BQuant Intraday Analytics. He also oversees data technologies for the financial services industry including Bloomberg’s partnership with Man Group to further develop the open source ArcticDB project and to enhance customers’ experience using the BQuant platform by integrating its capabilities.
Steven joined Bloomberg after a sixteen-year career on the buy-side as lead quant and portfolio manager. Most recently, he was Head of Quant Research Platform at AllianceBernstein (AB), a global asset manager with over $600 billion in AUM, where he and his team created an innovative quant platform that revolutionized AB’s multi-asset hedge fund business. Prior to this, Steven held numerous roles in quantitative research, portfolio management, trading, and quant development across multiple asset classes, including equity, currencies, commodities, rates, and volatility.
Ian Hummel
Ian Hummel is a Technical Product Manager for BQuant Enterprise working within the Office of the CTO. As part of his role in the Office of the CTO, he oversees strategic initiatives at the intersection of machine learning, big data, and cloud computing. Previous to Bloomberg, he led product initiatives in online marketing, identity federation, and enterprise search. He has a BA in Mathematics and Computer Science from Boston University and an MBA from INSEAD.
Ryuji Kanda
Ryuji Kanda is a Financial Developer/Quantitative Solutions Senior Specialist based in Japan.
He was based in U.K. from 2016 to 2021 – He advised and worked closely with clients on generating investment ideas, adopting systematic investment tools, and building various research processes alongside publishing news articles with quantitative analysis. Since 2021, his focus has shifted to working closely with financial institutes in Japan.
Before joining Bloomberg, he worked with data science/data analysis start-ups in Europe and Japan.